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A Study on Integrated Wavelet Neural Networks in Fault Diagnosis Based on Information Fusion

A Study on Integrated Wavelet Neural Networks in Fault Diagnosis Based on Information Fusion
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摘要 The tight wavelet neural network was constituted by taking the nonlinear Morlet wavelet radices as the excitation function. The idiographic algorithm was presented. It combined the advantages of wavelet analysis and neural networks. The integrated wavelet neural network fault diagnosis system was set up based on both the information fusion technology and actual fault diagnosis, which took the sub-wavelet neural network as primary diagnosis from different sides, then came to the conclusions through decision-making fusion. The realizable policy of the diagnosis system and established principle of the sub-wavelet neural networks were given. It can be deduced from the examples that it takes full advantage of diversified characteristic information, and improves the diagnosis rate. The tight wavelet neural network was constituted by taking the nonlinear Morlet wavelet radices as the excitation function. The idiographic algorithm was presented. It combined the advantages of wavelet analysis and neural networks. The integrated wavelet neural network fault diagnosis system was set up based on both the information fusion technology and actual fault diagnosis, which took the sub-wavelet neural network as primary diagnosis from different sides, then came to the conclusions through decision-making fusion. The realizable policy of the diagnosis system and established principle of the sub-wavelet neural networks were given. It can be deduced from the examples that it takes full advantage of diversified characteristic information, and improves the diagnosis rate.
作者 ANG Xue-ye
出处 《International Journal of Plant Engineering and Management》 2007年第1期42-48,共7页 国际设备工程与管理(英文版)
关键词 fault diagnosis wavelet analysis integrated neural network information fusion diagnosis rate fault diagnosis, wavelet analysis, integrated neural network, information fusion, diagnosis rate
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  • 1张玲,模式识别与人工智能,1994年,7卷,3期,191页 被引量:1
  • 2Zhang Q,IEEE Trans Neural Netw,1992年,3卷,6期,889页 被引量:1

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